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Dictionary learning and update based on simultaneous codeword optimization (SimCO)

Dai, W, Xu, T and Wang, W (2012) Dictionary learning and update based on simultaneous codeword optimization (SimCO)

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Abstract

Dictionary learning aims to adapt elementary codewords directly from training data so that each training signal can be best approximated by a linear combination of only a few codewords. Following the two-stage iterative processes: sparse coding and dictionary update, that are commonly used, for example, in the algorithms of MOD and K-SVD, we propose a novel framework that allows one to update an arbitrary set of codewords and the corresponding sparse coefficients simultaneously, hence termed simultaneous codeword optimization (SimCO). Under this framework, we have developed two algorithms, namely the primitive and the regularized SimCO. Simulations are provided to show the advantages of our approach over the K-SVD algorithm in terms of both learning performance and running speed. © 2012 IEEE.

Item Type: Conference or Workshop Item (Conference Paper)
Divisions : Faculty of Engineering and Physical Sciences > Electronic Engineering > Centre for Vision Speech and Signal Processing
Authors :
AuthorsEmailORCID
Dai, WUNSPECIFIEDUNSPECIFIED
Xu, TUNSPECIFIEDUNSPECIFIED
Wang, WUNSPECIFIEDUNSPECIFIED
Date : 2012
Identification Number : 10.1109/ICASSP.2012.6288309
Additional Information : © 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Depositing User : Symplectic Elements
Date Deposited : 17 Dec 2013 17:17
Last Modified : 09 Jun 2014 13:46
URI: http://epubs.surrey.ac.uk/id/eprint/804527

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